Fault-Tolerant Neuromorphic Computing With Memristors Using Functional ATPG for Efficient Recalibration-Reference-Cited by-同舟云学术

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Fault-Tolerant Neuromorphic Computing With Memristors Using Functional ATPG for Efficient Recalibration

Published:2023-08 Issue:4 Volume:40 Page:42-50
ISSN:2168-2356
Container-title:IEEE Design & Test
language:
Short-container-title:IEEE Des. Test

Author:

Ahmed Soyed Tuhin¹^ORCID,Tahoori Mehdi B.¹^ORCID

Affiliation:

1. Karlsruhe Institute of Technology, Karlsruhe, Germany

Funder

German Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Link

http://xplorestaging.ieee.org/ielx7/6221038/10153784/10115236.pdf?arnumber=10115236

Reference12 articles.

1. Equivalent-accuracy accelerated neural-network training using analogue memory

2. Accurate deep neural network inference using computational phase-change memory

3. XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks

4. Rescuing Memristor-based Neuromorphic Design with High Defects

5. Robust processing-in-memory neural networks via noise-aware normalization;tsai;arXiv 2007 03230,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks;2024 IEEE European Test Symposium (ETS);2024-05-20

2. Analysis of Memristor Neural Networks for fault Tolerant Computing;2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS);2024-04-18

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